Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/17178#discussion_r104971290
--- Diff: R/pkg/inst/tests/testthat/test_sparkSQL.R ---
@@ -1342,28 +1342,52 @@ test_that("column functions", {
df <- read.json(mapTypeJsonPath)
j <- collect(select(df, alias(to_json(df$info), "json")))
expect_equal(j[order(j$json), ][1], "{\"age\":16,\"height\":176.5}")
- df <- as.DataFrame(j)
- schema <- structType(structField("age", "integer"),
- structField("height", "double"))
- s <- collect(select(df, alias(from_json(df$json, schema), "structcol")))
- expect_equal(ncol(s), 1)
- expect_equal(nrow(s), 3)
- expect_is(s[[1]][[1]], "struct")
- expect_true(any(apply(s, 1, function(x) { x[[1]]$age == 16 } )))
-
- # passing option
- df <- as.DataFrame(list(list("col" = "{\"date\":\"21/10/2014\"}")))
- schema2 <- structType(structField("date", "date"))
- expect_error(tryCatch(collect(select(df, from_json(df$col, schema2))),
- error = function(e) { stop(e) }),
- paste0(".*(java.lang.NumberFormatException: For input
string:).*"))
- s <- collect(select(df, from_json(df$col, schema2, dateFormat =
"dd/MM/yyyy")))
- expect_is(s[[1]][[1]]$date, "Date")
- expect_equal(as.character(s[[1]][[1]]$date), "2014-10-21")
-
- # check for unparseable
- df <- as.DataFrame(list(list("a" = "")))
- expect_equal(collect(select(df, from_json(df$a, schema)))[[1]][[1]], NA)
+
+ schemas <- list(structType(structField("age", "integer"),
structField("height", "double")),
+ "struct<age:integer,height:double>")
--- End diff --
If it is a format that is documented and globally defined in Spark, it
shouldn't be a problem to use that. If a variant of `from_json` is already
taking a schema string, we should be able call it directly from R without
having to make it public, although it might make sense to do so, if, say,
`read.format("json")` supports it too.
I'm not super worry about having a way to produce JSON schema string for
the R user - we would just accept such JSON schema string and assume the user
can create it.
But yea, overall this feels very heavy-weighed to support multiple JSON
objects in a column value. How about just take a bool flag like we started
with? :)
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